Semester 1 Session 9a Planning and Forecasting Nature and purpose Objectives

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1 Management and Planning SEMESTER 1 September 2009 Semester 1 Session 9a Planning and Forecasting Nature and purpose Objectives To be able to describe and discuss What a Forecast is Reasons for Forecasting A Model of the Forecasting Process Important Strategic Forecasts An overview of Forecasting Methods in preparation for the next lecture on techniques And to apply these concepts in a given business scenario. These are all likely to be tested in coursework and in the examination. C:\Allwork\geoff\Modules\M&P\M&P session 9a header - P&F nature and purpose.doc Created by Geoff Leese

2 CE Planning & Forecasting - Nature & Purpose CE Management and Planning Diane Bishton K229 (d.k.bishton@staffs.ac.uk) Planning & Forecasting - Nature & purpose In this lecture We will Look at what a Forecast is Introduce some Reasons for Forecasting Consider a Model of the Forecasting Process and some Important Strategic Forecasts Finish with an Overview of Forecasting Methods in preparation for the next lecture on techniques Introduction Business, more than any other occupation, is a continual dealing with the future; it is continual calculation, an instinctive exercise in foresight Henry R. Luce Quoted in Strategic Business Forecasting (Shim J K, 2000)

3 CE Planning & Forecasting - Nature & Purpose What is a Forecast? A Prediction of some Future state or event May be based on past events, experience, quantitative or qualitative data May involve speculation Short-term normally more realistic than long-term - more uncertain >>> more error Internal & External Forecasts Internal - for the individual organisation itself e.g. Sales, equipment down-time, plant utilisation, cash flows External - about the business environment e.g. Exchange rates, consumer expenditure, competitor behaviour Why forecast? Without a forecast you have little foundation for planning e.g. Students taking Awards Open Systems, such as a business, need to respond to change & these responses may need some time - even years - to prepare for So why not put what you know to advantage? - Human memory & hence experience (often supplemented by technology) is a powerful tool for survival The objective of forecasting is to reduce risk in decision making (Shim, 2000)

4 CE Planning & Forecasting - Nature & Purpose A model of the forecasting process (adapted from Bennett 1999, p 80) What needs to be Forecast? Why, When, Who? Objectives What Forecasting Technique(s)? Methods Data sources, Research approach Analysis Identify Trends & Factors Evaluate information quality Evaluation Assess the forecast implications Monitor forecast accuracy 7 Important Strategic Forecasts - 1 Technology Forecasts New developments in products, processes, infrastructure Long-term management view combined with technology issues Methods - Brainstorming, Delphi Method, technology trends Outcomes - most optimistic, most pessimistic, most likely Approaches - Exploratory - assume steady progress Normative - possible radical change Important Strategic Forecasts - 2 Sales Forecasts To indicate expected revenue, which in turn suggests necessary materials, labour, plant etc, hence finance Methods - a wide range, but often based on past sales tempered by expected response to advertising, competitor behaviour etc. Examples : Regression Analysis, Moving Averages, Exponentially Weighted Moving Averages, Sales Staff estimates Data sources - past sales trends, market research, trade reports, Govt statistics

5 CE Planning & Forecasting - Nature & Purpose Sales Forecasts related to Managerial Functions (from Shim, 2000, p4) Sales Forecast Money & Credit Forecasts Financial Planning Capacity Planning Production Planning Capital Expenditure Marketing Planning Manpower Planning Inventory Planning Project Planning Procurement Planning An Overview of Forecasting Methods (from Shim, 2000, p6) Qualitative - Expert Opinion, Delphi Technique, Sales Force Polling, Consumer Surveys, PERT Derived Quantitative - Time Series (Historical) - Naïve Methods, Moving Averages, Exponential Smoothing, Trend Analysis, Decomposition of Time Series, Box-Jenkins - Associate (Causal) - Simple Regression, Multiple Regression, Econometric Modelling Markov Approach Indirect Method - Market Survey, I/O Analysis, Econometric Indicators 11 Further References Bennett R (1999) Corporate Strategy 2e Harlow UK: Pearson Education Ltd Shearer, P. (1994) Business Forecasting & Planning Hemel Hempstead: Prentice Hall Shim Jae K. (2000) Strategic Business Forecasting, revised edition Boca Raton, Florida: St Lucie Press John Galt's view

6 Management and Planning SEMESTER 1 September 2009 Semester 1 Session 9a Planning and Forecasting Nature and purpose Tutorial questions No tutorial set this week work on the assignment C:\Allwork\geoff\Modules\M&P\M&P session 9a tutorial - P&F nature and purpose.doc Created by Geoff Leese

7 Management and Planning SEMESTER 1 September 2009 Semester 1 Session 9b Forecasting and evaluation techniques Objectives To be able to describe and discuss The categories of forecasting techniques introduced in an earlier lecture Some common techniques : o Qualitative - Delphi Method o Quantitative - Time Series (Historical) e.g. Moving Averages Exponential Smoothing Trend Analysis that between them cover a range of forecasting periods And to apply these concepts in a given business scenario that includes USING the techniques taught. These are all likely to be tested in coursework and in the examination. C:\Allwork\geoff\Modules\M&P\M&P session 9b header - Forecasting and evaluation techniques.doc Created by Geoff Leese

8 CE Forecasting Techniques CE Management and Planning Diane Bishton/Geoff Leese Nov 2006 Forecasting Techniques 1 In this lecture We will Review the categories of forecasting techniques introduced in an earlier lecture Look at some common techniques : Qualitative - Delphi Method Quantitative - Time Series (Historical) e.g. Moving Averages, Exponential Smoothing, Trend Analysis that between them cover a range of forecasting periods 2 3 Introduction We have seen that Business, more than any other occupation, is a continual dealing with the future; it is continual calculation, an instinctive exercise in foresight Henry R. Luce Businesses need to respond to change & these responses may need some time - even years - to prepare for. Without a forecast you have little foundation for planning therefore as a precursor to planning, there is a need for forecasting. 1

9 CE Forecasting Techniques 4 An Overview of Forecasting Methods (from Shim, 2000, p6) Qualitative Expert Opinion, Delphi Technique, Sales Force Polling, Consumer Surveys, PERT Derived Quantitative Time Series (Historical) - Naïve Methods, Moving Averages, Exponential Smoothing, Trend Analysis, Decomposition of Time Series, Box-Jenkins Associate (Causal) - Simple Regression, Multiple Regression, Econometric Modelling Indirect Method Market Survey, I/O Analysis, Econometric Indicators Qualitative - Delphi Technique n Fair to Very Good for both short- & long-term forecasts (beyond 2 years) n Data required - a panel of experts is separately questioned via a sequence of questionnaires - the first is used to create the next & so on n A coordinator issues, edits & brings together the experts responses n Typical use - long-range sales & technology forecasts (see example from Shearer, 1994) 5 Simple Delphi example (from Shim 2000) From a 1982 survey of college students asked to estimate the population of Bombay (now Mumbai) India Forecast (m) > <2 Midpoint of forecast No. of Panelists (9.9m in 1981) Probability Distribution of Panelists Weighted average (col. 2 x col 4) Total m 6 2

10 CE Forecasting Techniques 7 Moving Averages npoor to Good for short-term (0-3 month) forecasts ndata required - a minimum of 2 years of history if seasonal factors apply, otherwise less data. More history usually gives a better forecast. Averages changed as more recent data is applied ntypical use - stock forecasting for lowvolume items Simple Moving Average The trend in a series is smoothed by, e.g. taking the average of the most recent 10 points. Formula SMA = sum(value for each period)/number of periods All periods are equally weighted in SMA 8 Simple moving average example 9 3

11 CE Forecasting Techniques Exponential Smoothing Fair to Very Good for short-term (0-3 month) forecasts Data required - same as Moving Average except that more recent data sources are given a higher weighting. More effective when demand is rather random & there s no seasonal variation in the data series Typical use - stock, production, sales forecasts 10 Exponential Smoothing The series is smoothed but the moving average is of infinite length & weights decrease further into the past instead of being equal for every data point EMA today = (value today * K) + ( EMA yesterday * (1-K)) Where K = 2 / (N+1) and N = number of periods. Eg (10 period EMA, this period value is 20, EMA last period is 15) K = 2/11 = EMA this period = (20*0.1818) + (15* ) = Exponential smoothing example Ten-day EMA 12 4

12 CE Forecasting Techniques Trend Analysis nvery Good for short-term (0-3 month) & Good for long-term (2 years +) forecasts ndata required - a minimum of 5 years to start ntypical use - products in the growth & maturity stages of their life cycle Maturity 13 No Of Units Growth Time Decline Introduction Trend Analysis Components Trend Analysis fits a trend line to time-series data. It s a special type of simple regression (estimates the average relationship between a dependent variable & any independent variable) Trend Component is the general upward or downward movement of an average over time Seasonal Component - recurring fluctuation that repeats (usually each year) Cyclical Component - like seasonal but longer than one year frequency Random Component - unpredictable 14 Trend Component example Trend : Copper prices (Shearer, 1994, p104) 15 5

13 CE Forecasting Techniques Seasonal Component example Seasonal : VCR Sales (Shim, 2000, p90) 16 Cyclical Component example 17 Cyclical : Residential building cycle index USA (Shearer, 1994, p 95) Further Reading nsee tutorial handout 18 6

14 Management and Planning SEMESTER 1 September 2009 Semester 1 Session 9b Forecasting and evaluation techniques Additional reading Bennett chapters 16 onwards Shearer, P. (1994) Business Forecasting & Planning Shim Jae K. (2000) Strategic Business Forecasting, revised edition CHAPTER 3 SPECIFICALLY Tutorial questions See attached sheet C:\Allwork\geoff\Modules\M&P\M&P session 9b tutorial - Forecasting and evaluation techniques.doc Created by Geoff Leese

15 Quantitative techniques exercise Management and planning module Here is a table of historic production figures for almost the last 2 years. Month Production Month Production Jan Jan Feb Feb Mar Mar Apr Apr May May Jun Jun Jul Jul Aug Aug Sep Sep Oct Oct Nov Nov Dec Dec-08 1) Plot the production figures as a line graph, and add a trend line. 2) What does the trend indicate? Is there evidence of a cyclical or a seasonal trend? 3) Add extra columns to the table, and calculate i) 3 month Simple Moving Average ii) 3 month Exponential Moving Average 4) Plot the moving average figures on the chart that you created earlier. 5) Comment on the value of adding the moving average lines. C:\Allwork\geoff\Modules\M&P\Quantitative techniques exercise.doc Created by Geoff Leese